Memory compression and thermal efficiency of quantum implementations of non-deterministic hidden Markov models

by   Thomas J. Elliott, et al.

Stochastic modelling is an essential component of the quantitative sciences, with hidden Markov models (HMMs) often playing a central role. Concurrently, the rise of quantum technologies promises a host of advantages in computational problems, typically in terms of the scaling of requisite resources such as time and memory. HMMs are no exception to this, with recent results highlighting quantum implementations of deterministic HMMs exhibiting superior memory and thermal efficiency relative to their classical counterparts. In many contexts however, non-deterministic HMMs are viable alternatives; compared to them the advantages of current quantum implementations do not always hold. Here, we provide a systematic prescription for constructing quantum implementations of non-deterministic HMMs that re-establish the quantum advantages against this broader class. Crucially, we show that whenever the classical implementation suffers from thermal dissipation due to its need to process information in a time-local manner, our quantum implementations will both mitigate some of this dissipation, and achieve an advantage in memory compression.



There are no comments yet.


page 1

page 2

page 3

page 4


Thermal Efficiency of Quantum Memory Compression

Quantum coherence allows for reduced-memory simulators of classical proc...

Quantum adaptive agents with efficient long-term memories

Central to the success of adaptive systems is their ability to interpret...

Strong and Weak Optimizations in Classical and Quantum Models of Stochastic Processes

Among the predictive hidden Markov models that describe a given stochast...

Quantum coarse-graining for extreme dimension reduction in modelling stochastic temporal dynamics

Stochastic modelling of complex systems plays an essential, yet often co...

A quantum learning approach based on Hidden Markov Models for failure scenarios generation

Finding the failure scenarios of a system is a very complex problem in t...

Thermodynamically-Efficient Local Computation: Classical and quantum information reservoirs and generators

The thermodynamics of modularity identifies how locally-implemented comp...

Quantum Optimisation of Complex Systems with a Quantum Annealer

We perform an in-depth comparison of quantum annealing with several clas...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.